Top

Data Mining and Knowledge Discovery

Published in:

22-02-2018

Interpretation of text patterns

Authors: Md Abul Bashar, Yuefeng Li

Published in: Data Mining and Knowledge Discovery | Issue 4/2018

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Patterns are used as a fundamental means to analyse data in many text mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and lack of grounded (e.g. a priori defined) semantics have made it difficult for a user to interpret and explore the patterns. An insight into the meanings of the patterns can benefit users in the process of exploring them. In this regard, this paper presents a model to automatically interpret patterns by achieving two goals: (1) providing the meanings of patterns in terms of ontology concepts and (2) providing a new method for generating and extracting features from an ontology to describe the relevant information more effectively. Taking advantage of a domain ontology and a set of relevant statistics (e.g. term frequency in a document, inverse term frequency in a domain ontology, etc.), our proposed model can give an insight into the hidden meanings of the patterns. The model is evaluated by comparing it with different baseline models on three standard datasets. The results show that the performance of the proposed model is significantly better than baseline models.

next article Labeled Phrase Latent Dirichlet Allocation and its online learning algorithm

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Available only for authorised users

http://bit.ly/2zlOLZX.

http://bit.ly/2hm1nf7.

http://bit.ly/2zlOLZX.

Afrati F, Gionis A, Mannila H (2004) Approximating a collection of frequent sets. In: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Seattle, WA, USA, pp 12–19

Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, vol 22. ACM, Washington, DC, USA, pp 207–216

Anderson JR (1983) A spreading activation theory of memory. J Verbal Learn Verbal Behav 22(3):261–295CrossRef

Banko M, Cafarella MJ, Soderland S, Broadhead M, Etzioni O (2007) Open information extraction from the web. In: Proceedings of the 20th international joint conference on artifical intelligence, vol 7. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp 2670–2676

Bayardo Jr RJ (1998) Efficiently mining long patterns from databases. In: ACM Sigmod record, vol 27. ACM, Seattle, Washington, USA, pp 85–93

Bengio Y, Ducharme R, Vincent P, Jauvin C (2003) A neural probabilistic language model. J Mach Learn Res 3(Feb):1137–1155MATH

Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022MATH

Bloehdorn S, Cimiano P, Hotho A (2006) Learning ontologies to improve text clustering and classification. From data and information analysis to knowledge engineering. Springer, Magdeburg, pp 334–341CrossRef

Brewster C, Alani H, Dasmahapatra S, Wilks Y (2004) Data driven ontology evaluation. In: International conference on language resources and evaluation (LREC 2004). Lisbon, Portugal

Buckley C, Voorhees EM (2000) Evaluating evaluation measure stability. In: Proceedings of the 23rd annual international ACM SIGIR conference on research and development in information retrieval. ACM, Athens, Greece, pp 33–40

Bunescu R, Mooney RJ (2006) Subsequence kernels for relation extraction. Advances in neural information processing systems. MIT Press, Cambridge, pp 171–178

Calders T, Goethals B (2007) Non-derivable itemset mining. Data Min Knowl Disc 14(1):171–206MathSciNetCrossRef

Calegari S, Pasi G (2013) Personal ontologies: generation of user profiles based on the yago ontology. Inf Process Manag 49(3):640–658CrossRef

Caropreso MF, Matwin S, Sebastiani F (2001) A learner-independent evaluation of the usefulness of statistical phrases for automated text categorization. Text Databases Doc Manag Theory Pract 5478:78–102

Chemudugunta C, Holloway A, Smyth P, Steyvers M (2008a) Modeling documents by combining semantic concepts with unsupervised statistical learning. In: International semantic web conference. Springer, Karlsruhe, pp 229–244

Chemudugunta C, Smyth P, Steyvers M (2008b) Combining concept hierarchies and statistical topic models. In: Proceedings of the 17th ACM conference on information and knowledge management, ACM, Napa Valley, California, USA, pp 1469–1470

Collins AM, Loftus EF (1975) A spreading-activation theory of semantic processing. Psychol Rev 82(6):407CrossRef

Collobert R, Weston J (2008) A unified architecture for natural language processing: Deep neural networks with multitask learning. In: Proceedings of the 25th international conference on machine learning, ACM, pp 160–167

Crestani F (1997) Application of spreading activation techniques in information retrieval. Artif Intell Rev 11(6):453–482CrossRef

Deerwester SC, Dumais ST, Landauer TK, Furnas GW, Harshman RA (1990) Indexing by latent semantic analysis. J Am Soc Inf Sci 41(6):391–407CrossRef

Del Corro L, Gemulla R (2013) Clausie: clause-based open information extraction. In: Proceedings of the 22nd international conference on world wide web. ACM, pp 355–366

Egozi O, Gabrilovich E, Markovitch S (2008) Concept-based feature generation and selection for information retrieval. In: AAAI conference on artificial intelligence, vol 8. Chicago, Illinois, pp 1132–1137

Egozi O, Markovitch S, Gabrilovich E (2011) Concept-based information retrieval using explicit semantic analysis. ACM Trans Inf Syst (TOIS) 29(2):1–38CrossRef

Fader A, Soderland S, Etzioni O (2011) Identifying relations for open information extraction. In: Proceedings of the conference on empirical methods in natural language processing, Association for Computational Linguistics, pp 1535–1545

Gabrilovich E, Markovitch S (2005) Feature generation for text categorization using world knowledge. In: Proceedings of the 19th international joint conference on artificial intelligence, vol 5. Edinburgh, Scotland, pp 1048–1053

Gabrilovich E, Markovitch S (2007a) Computing semantic relatedness using wikipedia-based explicit semantic analysis. In: Proceedings of the 20th international joint conference on artificial intelligence, vol 6. Hyderabad, India, pp 1606–1611

Gabrilovich E, Markovitch S (2007b) Harnessing the expertise of 70, 000 human editors: knowledge-based feature generation for text categorization. J Mach Learn Res 8(10):2297–2345

Gabrilovich E, Markovitch S (2009) Wikipedia-based semantic interpretation for natural language processing. J Artif Intell Res 34(2):443–498CrossRefMATH

Gallo A, De Bie T, Cristianini N (2007) Mini: mining informative non-redundant itemsets. In: European conference on principles of data mining and knowledge discovery. Springer, pp 438–445

Gauch S, Chaffee J, Pretschner A (2003) Ontology-based personalized search and browsing. Web Intell Agent Syst 1(3):219–234

Glorot X, Bordes A, Bengio Y (2011) Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 513–520

Goutsias J, Mahler RP, Nguyen HT (2012) Random sets: theory and applications, vol 97. Springer, Berlin

Grossman DA (2004) Information retrieval: algorithms and heuristics, vol 15. Springer, BerlinCrossRefMATH

Guns T, Nijssen S, De Raedt L (2013) k-pattern set mining under constraints. IEEE Trans Knowl Data Eng 25(2):402–418CrossRef

Han J, Wang J, Lu Y, Tzvetkov P (2002) Mining top-k frequent closed patterns without minimum support. In: IEEE international conference on data mining (ICDM), IEEE, Maebashi City, Japan, pp 211–218

Hennig L, Umbrath W, Wetzker R (2008) An ontology-based approach to text summarization. In: IEEE/WIC/ACM international joint conference on web intelligence (WI) and intelligent agent technology (IAT), vol 3. IEEE. Sydney, NSW, Australia, pp 291–294

Hofmann T (1999) Probabilistic latent semantic indexing. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval, ACM, pp 50–57

Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Nat Acad Sci 81(10):3088–3092CrossRefMATH

Hotho A, Nürnberger A, Paaß G (2005) A brief survey of text mining. Ldv Forum 20:19–62

Hulpus I, Hayes C, Karnstedt M, Greene D (2013) Unsupervised graph-based topic labelling using dbpedia. In: Proceedings of the sixth ACM international conference on Web search and data mining, ACM, Rome, Italy, pp 465–474

Ingaramo D, Pinto D, Rosso P, Errecalde M (2008) Evaluation of internal validity measures in short-text corpora. In: Computational linguistics and intelligent text processing, Springer, Haifa, Israel, pp 555–567

Karp RM (1972) Reducibility among combinatorial problems. Complexity of computer computations. Springer, Berlin, pp 85–103CrossRef

Knobbe AJ, Ho EK (2006) Pattern teams. In: European conference on principles of data mining and knowledge discovery, Springer, pp 577–584

Kriegel HP, Borgwardt KM, Kröger P, Pryakhin A, Schubert M, Zimek A (2007) Future trends in data mining. Data Min Knowl Disc 15(1):87–97MathSciNetCrossRef

Kruse R, Schwecke E, Heinsohn J (1991) Uncertainty and vagueness in knowledge based systems. Springer, New York Inc., New YorkCrossRefMATH

Kruse R, Schwecke E, Heinsohn J (2012) Uncertainty and vagueness in knowledge based systems: numerical methods. Springer, BerlinMATH

Lau JH, Newman D, Karimi S, Baldwin T (2010) Best topic word selection for topic labelling. In: Proceedings of the 23rd international conference on computational linguistics: Posters, Association for Computational Linguistics, Beijing, China, pp 605–613

Lau JH, Grieser K, Newman D, Baldwin T (2011) Automatic labelling of topic models. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol 1. Association for Computational Linguistics, Portland, Oregon, USA, pp 1536–1545

Lewis DD, Yang Y, Rose TG, Li F (2004) Rcv1: a new benchmark collection for text categorization research. J Mach Learn Res 5:361–397

Li G, Zaki MJ (2016) Sampling frequent and minimal boolean patterns: theory and application in classification. Data Min Knowl Disc 30(1):181–225MathSciNetCrossRef

Li Y, Zhong N (2006) Mining ontology for automatically acquiring web user information needs. IEEE Trans Knowl Data Eng 18(4):554–568CrossRef

Li Y, Algarni A, Zhong N (2010) Mining positive and negative patterns for relevance feature discovery. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Washington, DC, USA, pp 753–762

Li Y, Algarni A, Albathan M, Shen Y, Bijaksana MA (2015) Relevance feature discovery for text mining. IEEE Trans Knowl Data Eng 27(6):1656–1669. https://doi.org/10.1109/TKDE.2014.2373357 CrossRef

Liu B, Zhao K, Benkler J, Xiao W (2006) Rule interestingness analysis using olap operations. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Philadelphia, PA, USA, pp 297–306

Liu J, Shang J, Wang C, Ren X, Han J (2015) Mining quality phrases from massive text corpora. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data, ACM, pp 1729–1744

Liu J, Ren X, Shang J, Cassidy T, Voss CR, Han J (2016) Representing documents via latent keyphrase inference. In: Proceedings of the 25th international conference on world wide web, International World Wide Web Conferences Steering Committee, pp 1057–1067

Mao XL, Ming ZY, Zha ZJ, Chua TS, Yan H, Li X (2012) Automatic labeling hierarchical topics. In: Proceedings of the 21st ACM international conference on Information and knowledge management, ACM, pp 2383–2386

Mei Q, Liu C, Su H, Zhai C (2006a) A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In: Proceedings of the 15th international conference on world wide web, ACM, Edinburgh, Scotland, pp 533–542

Mei Q, Xin D, Cheng H, Han J, Zhai C (2006b) Generating semantic annotations for frequent patterns with context analysis. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Philadelphia, PA, USA, pp 337–346

Mei Q, Shen X, Zhai C (2007a) Automatic labeling of multinomial topic models. In: Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, San Jose, California, USA, pp 490–499

Mei Q, Xin D, Cheng H, Han J, Zhai C (2007b) Semantic annotation of frequent patterns. ACM Trans Knowl Discov Data (TKDD) 1(3):11:1–11:30

Michelson M, Macskassy SA (2010) Discovering users’ topics of interest on twitter: a first look. In: Proceedings of the fourth workshop on analytics for noisy unstructured text data, ACM, pp 73–80

Mielikäinen T, Mannila H (2003) The pattern ordering problem. In: European conference on principles of data mining and knowledge discovery, Springer, pp 327–338

Mihelčić M, Šimić G, Leko MB, Lavrač N, Džeroski S, Šmuc T (2017) Using redescription mining to relate clinical and biological characteristics of cognitively impaired and Alzheimer’s disease patients. PLoS ONE 12:1–35. https://doi.org/10.1371/journal.pone.0187364

Mikolov T (2012) Statistical language models based on neural networks. Presentation at google, mountain view, 2nd April

Mikolov T, Chen K, Corrado G, Dean J (2013a) Efficient estimation of word representations in vector space. In: International conference on learning representations (ICLR) workshop

Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J (2013b) Distributed representations of words and phrases and their compositionality. In: Advances in neural information processing systems, pp 3111–3119

Mikolov T, Yih Wt, Zweig G (2013c) Linguistic regularities in continuous space word representations. In: Proceedings of the 2013 conference of the North American chapter of the Association for Computational Linguistics: human language technologies (NAACL-HLT), vol 13, pp 746–751

Molchanov I (2006) Theory of random sets. Springer, Berlin

Navigli R, Velardi P, Gangemi A (2003) Ontology learning and its application to automated terminology translation. IEEE Intell Syst 18(1):22–31CrossRef

Parida L, Ramakrishnan N (2005) Redescription mining: structure theory and algorithms. In: AAAI, vol 5, pp 837–844

Parthasarathy S, Zaki MJ, Ogihara M, Dwarkadas S (1999) Incremental and interactive sequence mining. In: Proceedings of the eighth international conference on information and knowledge management, ACM, Kansas City, Missouri, USA, pp 251–258

Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Discovering frequent closed itemsets for association rules. In: Proceedings of the 7th international conference on database theory. Springer, London, UK, pp 398–416

Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), vol 14, pp 1532–1543

Porter MF (1980) An algorithm for suffix stripping. Program Electron Libr Inf Syst 14(3):130–137CrossRef

Quillan MR (1966) Semantic memory. Technical report, DTIC Document

Raedt LD, Zimmermann A (2007) Constraint-based pattern set mining. In: Proceedings of the 2007 SIAM international conference on data mining, SIAM, pp 237–248

Ramakrishnan N, Kumar D, Mishra B, Potts M, Helm RF (2004) Turning cartwheels: an alternating algorithm for mining redescriptions. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, ACM, pp 266–275

Robertson SE, Soboroff I (2002) The trec 2002 filtering track report. In: TREC, vol 2002, Gaithersburg, Maryland, USA, pp 27–39

Rocchio JJ (1971) Relevance feedback in information retrieval. The smart retrieval system-experiments in automatic document processing, pp 313–323

Rose T, Stevenson M, Whitehead M (2002) The reuters corpus volume 1—from yesterday’s news to tomorrow’s language resources. In: Proceedings of the third international conference on language resources and evaluation (LREC), vol 2, Canary Islands, Spain, pp 827–832

Ruggieri S (2010) Frequent regular itemset mining. In: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, pp 263–272

Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. Cognit Model 5(3):1MATH

Salton G (1968) Automatic information organization and retrieval. McGraw-Hill, New York

Schmitz M, Bart R, Soderland S, Etzioni O, et al (2012) Open language learning for information extraction. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning, Association for Computational Linguistics, pp 523–534

Schwenk H (2007) Continuous space language models. Comput Speech Lang 21(3):492–518CrossRef

Shen Y, Li Y, Xu Y (2012) Adopting relevance feature to learn personalized ontologies. In: Australasian joint conference on artificial intelligence, Springer, Sydney, Australia, pp 457–468

Siebes A, Vreeken J, Leeuwen Mv (2006) Item sets that compress. In: Proceedings of the 2006 SIAM international conference on data mining, SIAM, pp 395–406

Sieg A, Mobasher B, Burke R (2007) Web search personalization with ontological user profiles. In: Proceedings of the sixteenth ACM conference on information and knowledge management, ACM, Lisbon, Portugal, pp 525–534

Socher R, Lin CC, Manning C, Ng AY (2011) Parsing natural scenes and natural language with recursive neural networks. In: Proceedings of the 28th international conference on machine learning (ICML-11), pp 129–136

Song Y, Wang H, Wang Z, Li H, Chen W (2011) Short text conceptualization using a probabilistic knowledgebase. In: Proceedings of the twenty-second international joint conference on artificial intelligence—vol 3. AAAI Press, Barcelona, pp 2330–2336

Spasic I, Ananiadou S, McNaught J, Kumar A (2005) Text mining and ontologies in biomedicine: making sense of raw text. Brief Bioinform 6(3):239–251CrossRef

Sun X, Xiao Y, Wang H, Wang W (2015) On conceptual labeling of a bag of words. In: Proceedings of the 24th international conference on artificial intelligence. AAAI Press, Buenos Aires, pp 1326–1332

Tan AH, et al (1999) Text mining: the state of the art and the challenges. In: Proceedings of the PAKDD 1999 workshop on knowledge discovery from advanced databases, vol 8, pp 65–70

Tao X, Li Y, Zhong N (2011) A personalized ontology model for web information gathering. IEEE Trans Knowl Data Eng 23(4):496–511CrossRef

Thiel K, Berthold MR (2010) Node similarities from spreading activation. In: 10th international conference on data mining (ICDM). IEEE, pp 1085–1090

Tran T, Cimiano P, Rudolph S, Studer R (2007) Ontology-based interpretation of keywords for semantic search. The semantic web, pp 523–536

Turney PD (2013) Distributional semantics beyond words: supervised learning of analogy and paraphrase. Trans Assoc Comput Linguist 1:353–366

Turney PD, Pantel P (2010) From frequency to meaning: vector space models of semantics. J Artif Intell Res 37:141–188MathSciNetCrossRefMATH

Verma R, Chen P, Lu W (2007) A semantic free-text summarization system using ontology knowledge. In: Proceedings of document understanding conference, Citeseer, Rochester, New York, USA, p 5

Vo DT, Bagheri E (2016) Open information extraction. Encycl Semant Comput Robot Intell. https://doi.org/10.1142/S2425038416300032

Wang P, Domeniconi C (2008) Building semantic kernels for text classification using wikipedia. In: Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Las Vegas, Nevada, USA, pp 713–721

Wang X, McCallum A (2006) Topics over time: a non-Markov continuous-time model of topical trends. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Philadelphia, PA, USA, pp 424–433

Weston J, Bengio S, Usunier N (2011) Wsabie: Scaling up to large vocabulary image annotation. In: Proceedings of the twenty-second international joint conference on artificial intelligence, vol 11, pp 2764–2770

Wortsman J, Matsuoka LY, Chen TC, Lu Z, Holick MF (2000) Decreased bioavailability of vitamin D in obesity. Am J Clin Nutr 72(3):690–693CrossRef

Wu F, Weld DS (2010) Open information extraction using wikipedia. In: Proceedings of the 48th annual meeting of the association for computational linguistics, Association for Computational Linguistics, pp 118–127

Wu ST (2007) Knowledge discovery using pattern taxonomy model in text mining. PhD thesis, Electrical Engineering and Computer Science, Queensland University of Technology

Wu ST, Li Y, Xu Y (2006) Deploying approaches for pattern refinement in text mining. In: Sixth international conference on data mining, ICDM’06, IEEE, pp 1157–1161

Xin D, Han J, Yan X, Cheng H (2005) Mining compressed frequent-pattern sets. In: Proceedings of the 31st international conference on very large data bases, VLDB Endowment, Trondheim, Norway, pp 709–720

Xue GR, Zeng HJ, Chen Z, Yu Y, Ma WY, Xi W, Fan W (2004) Optimizing web search using web click-through data. In: Proceedings of the thirteenth ACM international conference on Information and knowledge management, ACM, pp 118–126

Yan X, Cheng H, Han J, Xin D (2005) Summarizing itemset patterns: a profile-based approach. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, ACM, Chicago, Illinois, USA, pp 314–323

Yi K, Chan LM (2009) Linking folksonomy to library of congress subject headings: an exploratory study. J Doc 65(6):872–900CrossRef

Zaki MJ, Ramakrishnan N (2005) Reasoning about sets using redescription mining. In: Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, ACM, pp 364–373

Zhong N, Li Y, Wu ST (2012) Effective pattern discovery for text mining. IEEE Trans Knowl Data Eng 24(1):30–44CrossRef

Zhou G, Qian L, Fan J (2010) Tree kernel-based semantic relation extraction with rich syntactic and semantic information. Inf Sci 180(8):1313–1325MathSciNetCrossRef

Zhu J, Nie Z, Liu X, Zhang B, Wen JR (2009) Statsnowball: a statistical approach to extracting entity relationships. In: Proceedings of the 18th international conference on World wide web, ACM, pp 101–110

Title: Interpretation of text patterns
Authors: Md Abul Bashar
Yuefeng Li
Publication date: 22-02-2018
Publisher: Springer US
Published in: Data Mining and Knowledge Discovery / Issue 4/2018
Print ISSN: 1384-5810
Electronic ISSN: 1573-756X
DOI: https://doi.org/10.1007/s10618-018-0556-z

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 4/2018

SICA: subjectively interesting component analysis

Anytime parallel density-based clustering

Cluster-based trajectory segmentation with local noise

Graph sampling with applications to estimating the number of pattern embeddings and the parameters of a statistical relational model

Labeled Phrase Latent Dirichlet Allocation and its online learning algorithm

Anomaly detection in spatiotemporal data via regularized non-negative tensor analysis

Premium Partner